In an automated manufacturing environment that performs batch processing, determining a cause of a low yield of specific items within the batch can prove difficult.
Two systems that use SPC to sample and quality test manufactured products are disclosed in U.S. Pat. No. 6,141,647 issued to Meijer et al. ('647 patent), and U.S. Pat. No. 6,345,259 B1 issued to Sandoval ('259 patent). The systems disclosed in the '647 and the '259 patent provide a manufacturing execution system that operates to determine the quality of batch processed manufactured items and then adjusts the quantity of future batch output based on the quality results determined using SPC processing. The systems disclosed in the '647 and the '259 patent use the SPC data as acceptance sampling for a batch and feed that information back to an ordering system. However, the '647 and the '259 operates at a batch level and is not able to diagnose a problem in the batch processing that is correlated with a low item yield.
In a semiconductor fabrication facility that processes wafers in batches of wafers called lots, finding a correlation between an abnormal event and resulting individual wafer yield of wafers disposed within a lot can be difficult. Additionally, finding a root cause of low wafer yield using lot-based data is difficult. Efforts are continually used to improve wafer yield, however, existing systems and methods do not provide a good way to monitor or determine whether wafer yield is actually improved based on properly diagnosing abnormal lots.
Typically abnormal events occurring in a wafer manufacturing process are diagnosed at the lot level, not at the wafer level. However, existing systems do not provide a system or method to correlate the occurrence of an abnormal event with individual wafers disposed within a lot even though, typically, when an abnormal event occurs, only a portion of a lot, rather than an entire lot is affected. Thus, it is desirable to provide a system that can correlate abnormal event occurrences with individual wafers impacted by the abnormal occurrence and analyze the results of the correlation to improve wafer yield.